(214 days)
Yes
The document explicitly states the device uses "artificial intelligence/deep learning technology" and a "locked artificial intelligence algorithm" to analyze chest X-ray images.
No
The device is solely used to identify and prioritize cases with suspected pneumothorax for radiologists, and its results cannot be used as a substitute for diagnosis or for clinical decision-making.
No
The device is explicitly stated to not be a substitute for diagnosis by a radiologist, nor can it be used on a stand-alone basis for clinical decision-making. Its purpose is to identify cases with suspected pneumothorax to prioritize them for radiologist review, acting as a triage tool rather than providing a diagnosis itself.
Yes
The device is explicitly described as "software as medical device (SaMD)" and a "web-based medical device using a locked artificial intelligence algorithm." The description focuses solely on the software's function of analyzing images and interacting with PACS, with no mention of accompanying hardware components that are part of the medical device itself.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices used to examine specimens taken from the human body, such as blood, urine, or tissue, to provide information about a person's health. This information is used for diagnosis, monitoring, or screening.
- Device Function: The QOCA® image Smart CXR Image Processing System analyzes medical images (chest X-rays), which are acquired directly from the patient's body using imaging equipment. It does not analyze specimens taken from the body.
- Intended Use: The intended use is to analyze chest X-ray images to identify suspected pneumothorax and prioritize cases for radiologist review. This is an image processing and analysis function, not an in vitro diagnostic test.
Therefore, while it is a medical device that aids in the diagnostic process, it does so by analyzing images, not by performing tests on biological specimens.
No
The provided text does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The "Control Plan Authorized (PCCP) and relevant text" section explicitly states "Not Found".
Intended Use / Indications for Use
QOCA® image Smart CXR Image Processing System is a software as medical device (SaMD) used, through artificial intelligence/deep learning technology, to analyze chest X-ray images of adult patient, and then identify cases with suspected pneumothorax. This product shall be used in conjunction with Picture Archiving and Communication System (PACS) at the hospital. This product will automatically analyze the DICOM files automatically pushed from PACS, and then make a notation next to the cases with suspected pneumothorax. This product is only used to remind radiologists to prioritize reviewing cases with suspected pneumothorax. Its results cannot be used as a substitute for a diagnosis by a radiologist, nor can it be used on a stand-alone basis for clinical decision-making.
Product codes
QFM
Device Description
This product, QOCA® image Smart CXR Image Processing System, is a web-based medical device using a locked artificial intelligence algorithm. It provides features such as cases sorting and image viewing, and supports multiple users at a time.
After connecting to Picture Archiving and Communication System (PACS) at the hospital, this product is capable of automatically analyzing either posteroanterior (PA) view or anteroposterior (AP) erect view chest X-ray images automatically pushed from PACS. Once a case with suspected pneumothorax is identified, a notation will be made next to the case in question, so the radiologist can prioritize to review cases with suspected pneumothorax in the Viewer Page. This product will not directly indicate, however, the specific portions or anomalies on the image.
Bases on the results of the standalone performance assessment, this product achieves, identification accuracy of AUC > 95% with Sensitivity > 91% and Specificity > 92%.
The dataset used for training the algorithm was independent of the testing dataset. The training dataset included various characteristics, such as age, gender, radiographic positioning, radiography device, etc.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
X-Ray
Anatomical Site
Chest
Indicated Patient Age Range
Adult patient
Intended User / Care Setting
Radiologist / hospital
Description of the training set, sample size, data source, and annotation protocol
The training dataset is used to train the model, and divided into three sets: the training set, the validation set, and the test set. All data was carefully managed to prevent overlap and ensure that each dataset was completely independent by using accession numbers and patient IDs. The model training dataset was collected from two hospitals, and additional data from the US National Institutes of Health (NIH) was added to the test set to improve its US patient population representativeness during training.
Description of the test set, sample size, data source, and annotation protocol
The performance assessment dataset is used to valid the model's performance. By using a separate performance assessment dataset, we can get a better idea of how well the model will perform in the real world. For performance assessment, the US patient population data form MIMIC dataset and a medical institution independent of model training dataset were used. To avoid any potential biases, the data for the model training dataset and the performance assessment dataset were carefully chosen and separated.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
The performance of the subject device, QOCA® image Smart CXR Image Processing System, has been validated in two separate pivotal studies. The two studies were performed with the data from the MIMIC dataset and a Taiwanese hospital respectively. The performance of the subject device across the performance assessment dataset achieves an area under the curve (AUC) of 97.8% (95% CI: [97.0%. 98.5%]: in addition, the sensitivity and specificity achieves 92.5% (95% CI: [90.5%, 94.2%]), 94.0% (95% CI: [93.9%, 94.6%]) respectively, without subgroup breakdown.
First, the MIMIC dataset was used to demonstrate the generalizability of the device to the demographics of the US population. The dataset consisted of 3,105 radiographs with 336 positive and 2,769 negative pneumothorax cases. The ethnicities included Asian, Black/African American, Hispanic or Latino, and White. The dataset was truthed by three radiologists. The performance of the subject device to the MIMIC dataset is AUC of 97.7% (95% CI: [96.5%, 98.8%]), the sensitivity and specificity is 93.7% (95% CI: [90.6%, 96.0%]) and 93.3% (95% CI: [92.3%, 94.2%]), respectively.
Second, the additional Taiwanese dataset was used to demonstrate the generalizability to different imaging equipment. The dataset consisted of 2,947 radiographs with 472 positive and 2,475 negative pneumothorax cases. The dataset was truthed by three radiologists. The performance of the subject device to the Taiwanese dataset is AUC of 97.4% (95% CI: [96.9%, 98.7%]), the sensitivity and specificity is 91.7% (95% CI: [88.8%, 94.0%]) and 94.9% (95% CI: [93.9%, 95.7%]), respectively.
Besides, we assessed the performance time of the subject device that reflects the time it takes for the device to analyze the study and send a notification to the worklist. The average performance time of the subject device was 4.94 seconds, and that is substantially equivalent to the predicate (22.1 seconds).
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Overall: AUC 97.8% (95% CI: [97.0%. 98.5%]), Sensitivity 92.5% (95% CI: [90.5%, 94.2%]), Specificity 94.0% (95% CI: [93.9%, 94.6%])
MIMIC dataset: AUC 97.7% (95% CI: [96.5%, 98.8%]), Sensitivity 93.7% (95% CI: [90.6%, 96.0%]), Specificity 93.3% (95% CI: [92.3%, 94.2%])
Taiwanese dataset: AUC 97.4% (95% CI: [96.9%, 98.7%]), Sensitivity 91.7% (95% CI: [88.8%, 94.0%]), Specificity 94.9% (95% CI: [93.9%, 95.7%])
Average performance time: 4.94 seconds
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2080 Radiological computer aided triage and notification software.
(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" followed by the words "U.S. Food & Drug Administration".
Quanta Computer Inc. % Joe Wang Research Specialist No. 188, Wenhua 2nd Rd., Guishan Dist. Taoyuan City, 33383 TAIWAN
January 27, 2023
Re: K221868
Trade/Device Name: QOCA® image Smart CXR Image Processing System Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: December 9, 2022 Received: December 19, 2022
Dear Joe Wang:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
1
devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
Jessica Lamb
Jessica Lamb, Ph.D. Assistant Director Imaging Software DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Traditional 510(k), K221868.AI2, Supplement 4 Section 4 - Indications for Use Statement (Form FDA 3881)
Supplement 4
Section 4 - Indications for Use Statement
(Form FDA 3881)
3
DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
Indications for Use
510(k) Number (if known) K221868
Device Name
QOCA® image Smart CXR Image Processing System
Indications for Use (Describe)
QOCA® image Smart CXR Image Processing System is a software as medical device (SaMD) used, through artificial intelligence/deep learning technology, to analyze chest X-ray images of adult patient, and then identify cases with suspected pneumothorax. This product shall be used in conjunction with Picture Archiving and Communication System (PACS) at the hospital. This product will automatically analyze the DICOM files automatically pushed from PACS, and then make a notation next to the cases with suspected pneumothorax. This product is only used to remind radiologists to prioritize reviewing cases with suspected pneumothorax. Its results cannot be used as a substitute for a diagnosis by a radiologist, nor can it be used on a stand-alone basis for clinical decision-making.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| ☑ Prescription Use (Part 21 CFR 801 Subpart D) | ☐ Over-The-Counter Use (21 CFR 801 Subpart C) |
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4
Traditional 510(k), K221868.AI2, Supplement 5 Section 5 - 510(k) Summary
Supplement 5
Section 5 - 510(k) Summary
5
Traditional 510(k), K221868.AI2, Supplement 5 Section 5 - 510(k) Summary
510(k) SUMMARY
| 5.1 Type of Submission: | Traditional |
|---|---|
| 5.2 Date of Summary: | 01/19/2023 |
| 5.3 Submitter: | Quanta Computer Inc. |
| Address: | No. 188, Wenhua 2nd Rd., Guishan Dist., Taoyuan City 33383, Taiwan (R.O.C) |
| Phone: | +886-3-327-2345 |
| Contact: | Joe Wang joe_wang@quantatw.com |
5.4 Identification of the Device: Proprietary/Trade Name:
| Proprietary/Trade Name: | QOCA® image Smart CXR Image
Processing System |
|-------------------------|-----------------------------------------------------------------------|
| Model Number: | ZSWC001 |
| Regulation Description: | Radiological Computer-Assisted
Prioritization Software For Lesions |
| Review Panel: | Radiology |
| Regulation Number: | 892.2080 |
| Product Code: | QFM |
| Device Class: | II |
ર.ડ Identification of the Predicate Device:
| Predicate Device Name: | HealthPNX |
|---|---|
| Model Number: | — |
| 510(k) Number: | K190362 |
| Manufacturer: | Zebra Medical Vision Ltd. |
| Regulation Number: | 892.2080 |
| Product Code: | QFM |
| Device Class: | II |
6
5.6 Intended Use/Indications for Use of the Device
QOCA® image Smart CXR Image Processing System is a software as medical device (SaMD) used, through artificial intelligence/deep learning technology, to analyze chest X-ray images of adult patient, and then identify cases with suspected pneumothorax. This product shall be used in conjunction with Picture Archiving and Communication System (PACS) at the hospital. This product will automatically analyze the DICOM files automatically pushed from PACS, and then make a notation next to the cases with suspected pneumothorax. This product is only used to remind radiologists to prioritize reviewing cases with suspected pneumothorax. Its results cannot be used as a substitute for a diagnosis by a radiologist, nor can it be used on a stand-alone basis for clinical decision-making.
5.7 Device Description
This product, QOCA® image Smart CXR Image Processing System, is a web-based medical device using a locked artificial intelligence algorithm. It provides features such as cases sorting and image viewing, and supports multiple users at a time.
After connecting to Picture Archiving and Communication System (PACS) at the hospital, this product is capable of automatically analyzing either posteroanterior (PA) view or anteroposterior (AP) erect view chest X-ray images automatically pushed from PACS. Once a case with suspected pneumothorax is identified, a notation will be made next to the case in question, so the radiologist can prioritize to review cases with suspected pneumothorax in the Viewer Page. This product will not directly indicate, however, the specific portions or anomalies on the image.
Bases on the results of the standalone performance assessment, this product achieves, identification accuracy of AUC > 95% with Sensitivity > 91% and Specificity > 92%.
The dataset used for training the algorithm was independent of the testing dataset. The training dataset included various characteristics, such as age, gender, radiographic positioning, radiography device, etc.
7
5.8 Comparison of Technological Characteristics with the Predicate Device
QOCA® image Smart CXR Image Processing System submitted in this 510(k) file is substantially equivalent in intended use, safety and performance to the cleared HealthPNX (K190362). Differences between the devices cited in this section do not raise any new issue of substantial equivalence.
| Item | Subject device | Predicate device | Substantial
equivalence
determination |
|----------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 510(k) No. | K221868 | K190362 | - |
| Proprietary Name | QOCA® image Smart CXR
Image Processing System | HealthPNX | - |
| Manufacturer | Quanta Computer Inc. | Zebra Medical Vision Ltd. | - |
| Regulation
Number | 21 CFR 892.2080 | 21 CFR 892.2080 | Same |
| Product Code | QFM | QFM | Same |
| Classification | Class II | Class II | Same |
| Intended Use | QOCA® image Smart CXR
Image Processing System is a
software as medical device
(SaMD) used, through artificial
intelligence/deep learning
technology, to analyze chest
X-ray images of adult patient,
and then identify cases with
suspected pneumothorax. This
product shall be used in
conjunction with Picture
Archiving and Communication
System (PACS) at the hospital.
This product will automatically
analyze the DICOM files | The Zebra Pneumothorax device
is a software workflow tool
designed to aid the clinical
assessment of adult Chest X-Ray
cases with features suggestive of
Pneumothorax in the medical
care environment.
HealthPNX analyzes cases using
an artificial intelligence
algorithm to identify suspected
findings.
It makes case-level output
available to a PACS/workstation
for worklist prioritization or
triage. HealthPNX is not | Similar
Both devices are
intended to aid in
worklist triage by
providing notification
of suspected
pneumothorax cases
using an artificial
intelligence algorithm.
They are not intended
to be used on a
stand-alone basis for
clinical
decision-making or
clinical diagnosis. |
| Item | Subject device | Predicate device | Substantial
equivalence
determination |
| Notification-only,
parallel workflow
tool | automatically pushed from
PACS, and then make a notation
next to the cases with suspected
pneumothorax. This product is
only used to remind radiologists
to prioritize reviewing cases with
suspected pneumothorax. Its
results cannot be used as a
substitute for a diagnosis by a
radiologist, nor can it be used on
a stand-alone basis for clinical
decision-making. | intended to direct attention to
specific portions or anomalies of
an image. Its results are not
intended to be used on a
stand-alone basis for clinical
decision-making nor is it
intended to rule out
Pneumothorax or otherwise
preclude clinical assessment of
X-Ray cases | Same |
| User | Radiologist | Radiologist | Same |
| Radiological image
format | DICOM | DICOM | Same |
| Identify patients
with prespecified
clinical condition | Yes | Yes | Same |
| Clinical condition | Pneumothorax | Pneumothorax | Same |
| Alert to finding | Passive notification flagged
for review | Passive notification flagged
for review | Same |
| Independent of
standard of care
workflow | Yes; No cases are removed
from worklist | Yes; No cases are removed
from worklist | Same |
| Modality | X-Ray | X-Ray | Same |
| Body part | Chest | Chest | Same |
| Item | Subject device | Predicate device | Substantial
equivalence
determination |
| Artificial
Intelligence
algorithm | Yes | Yes | Same |
| Limited to analysis
of imaging data | Yes | Yes | Same |
| Aids prompt
identification of
cases with indicated
findings | Yes | Yes | Same |
| Where results are
received | Workstation | PACS/Workstation | Similar
The subject device will
be connected with
PACS and receives
patients' chest X-ray
images. The results will
only be presented on
the workstation.
It will not raise any
new issues of safety or
efficacy. |
| Time-to-notification | The average performance time
is 4.94 seconds. | The average performance time
is 22.1 seconds. | Same
Both devices can
provide effective
triage. |
8
Traditional 510(k), K221868.AI2, Supplement 5 Section 5 - 510(k) Summary
9
Traditional 510(k), K221868.AI2, Supplement 5 Section 5 - 510(k) Summary
10
Similarity and Difference
The subject device has the similar intended use to the predicate device. Both devices are intended to aid in worklist triage by providing notification of suspected pneumothorax cases using an artificial intelligence algorithm. And they are not intended to be used on a stand-alone basis for clinical decision-making or clinical diagnosis.
The slight difference between the subject device and the predicate is the result presentation. However, the result presenting of the subject device is within the scope of the predicate. Therefore, it will not affect the substantial equivalence.
5.9 Performance Data
The subject product, OOCA® image Smart CXR Image Processing System has been evaluated and verified in accordance with software specifications and applicable performance standards to ensure performance.
The separation of the model training dataset and performance assessment dataset
We split dataset into two parts: a model training dataset and a performance assessment dataset. The training dataset is used to train the model, and divided into three sets: the training set, the validation set, and the test set. The performance assessment dataset is used to valid the model's performance. By using a separate performance assessment dataset, we can get a better idea of how well the model will perform in the real world.
All data was carefully managed to prevent overlap and ensure that each dataset was completely independent by using accession numbers and patient IDs. The model training dataset was collected from two hospitals, and additional data from the US National Institutes of Health (NIH) was added to the test set to improve its US patient population representativeness during training. For performance assessment, the US patient population data form MIMIC dataset and a medical institution independent of model training dataset were used. This helped us get a better idea of how well the model would perform in the real world. To avoid any potential biases,
11
the data for the model training dataset and the performance assessment dataset were carefully chosen and separated.
Performance assessment
The performance of the subject device, QOCA® image Smart CXR Image Processing System, has been validated in two separate pivotal studies. The two studies were performed with the data from the MIMIC dataset and a Taiwanese hospital respectively. The performance of the subject device across the performance assessment dataset achieves an area under the curve (AUC) of 97.8% (95% CI: [97.0%. 98.5%]: in addition, the sensitivity and specificity achieves 92.5% (95% CI: [90.5%, 94.2%]), 94.0% (95% CI: [93.9%, 94.6%]) respectively, without subgroup breakdown. This performance is substantially equivalent to the predicate device (K190362); AUC of 98.3% (95% CI: [97.40%, 99.02%]), the sensitivity and specificity is 93.15% (95% CI: [87.76%, 96.67%]) and 92.99% (95% CI: [90.19%, 95.19%]), respectively.
First, the MIMIC dataset was used to demonstrate the generalizability of the device to the demographics of the US population. The dataset consisted of 3,105 radiographs with 336 positive and 2,769 negative pneumothorax cases. The ethnicities included Asian, Black/African American, Hispanic or Latino, and White. The dataset was truthed by three radiologists. The performance of the subject device to the MIMIC dataset is AUC of 97.7% (95% CI: [96.5%, 98.8%]), the sensitivity and specificity is 93.7% (95% CI: [90.6%, 96.0%]) and 93.3% (95% CI: [92.3%, 94.2%]), respectively.
| Characteristics | Subset | Quantity |
|---|---|---|
| Age | $22 \le age |